Volume 42, Issue 1
RESEARCH ARTICLE

A unified partial likelihood approach for X‐chromosome association on time‐to‐event outcomes

Wei Xu

Corresponding Author

E-mail address: Wei.Xu@uhnresearch.ca

Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, ON, Canada

Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada

Correspondence

Wei Xu, Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, ON M5G2M9, Canada.

Email: Wei.Xu@uhnresearch.ca

Meiling Hao, Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.

Email: Meiling.Hao@uhnresearch.ca

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Meiling Hao

Corresponding Author

E-mail address: Meiling.Hao@uhnresearch.ca

Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, ON, Canada

Correspondence

Wei Xu, Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, ON M5G2M9, Canada.

Email: Wei.Xu@uhnresearch.ca

Meiling Hao, Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.

Email: Meiling.Hao@uhnresearch.ca

Search for more papers by this author
First published: 26 November 2017
Citations: 5

Funding information: Grant sponsor: Canadian Institutes of Health Research (CIHR) Grant number: 145546.

Abstract

The expression of X‐chromosome undergoes three possible biological processes: X‐chromosome inactivation (XCI), escape of the X‐chromosome inactivation (XCI‐E), and skewed X‐chromosome inactivation (XCI‐S). Although these expressions are included in various predesigned genetic variation chip platforms, the X‐chromosome has generally been excluded from the majority of genome‐wide association studies analyses; this is most likely due to the lack of a standardized method in handling X‐chromosomal genotype data. To analyze the X‐linked genetic association for time‐to‐event outcomes with the actual process unknown, we propose a unified approach of maximizing the partial likelihood over all of the potential biological processes. The proposed method can be used to infer the true biological process and derive unbiased estimates of the genetic association parameters. A partial likelihood ratio test statistic that has been proved asymptotically chi‐square distributed can be used to assess the X‐chromosome genetic association. Furthermore, if the X‐chromosome expression pertains to the XCI‐S process, we can infer the correct skewed direction and magnitude of inactivation, which can elucidate significant findings regarding the genetic mechanism. A population‐level model and a more general subject‐level model have been developed to model the XCI‐S process. Finite sample performance of this novel method is examined via extensive simulation studies. An application is illustrated with implementation of the method on a cancer genetic study with survival outcome.

Number of times cited according to CrossRef: 5

  • Competing risk modeling and testing for X-chromosome genetic association, Computational Statistics & Data Analysis, 10.1016/j.csda.2020.107007, (107007), (2020).
  • A finite mixture model for X‐chromosome association with an emphasis on microbiome data analysis, Genetic Epidemiology, 10.1002/gepi.22190, 43, 4, (427-439), (2019).
  • A novel model for the X-chromosome inactivation association on survival data, Statistical Methods in Medical Research, 10.1177/0962280219859037, (096228021985903), (2019).
  • Pilot genome-wide association study identifying novel risk loci for type 2 diabetes in a Maya population, Gene, 10.1016/j.gene.2018.08.041, 677, (324-331), (2018).
  • Partial likelihood ratio test for X‐chromosome association models, Genetic Epidemiology, 10.1002/gepi.22157, 42, 8, (846-848), (2018).

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